Products related to Python:
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Foundational Python for Data Science
Data science and machine learning—two of the world's hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines.Python, the world's #1 programming language, is also the most popular language for data science and machine learning.This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master.Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.
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Fast Python for Data Science
Fast Python for Data Science is a hands-on guide to writing Python code that can process more data, faster, and with less resources.It takes a holistic approach to Python performance, showing you how your code, libraries, and computing architecture interact and can be optimized together. Written for experienced practitioners, Fast Python for Data Science dives right into practical solutions for improving computation and storage efficiency.You'll experiment with fun and interesting examples such as rewriting games in lower-level Cython and implementing a MapReduce framework from scratch.Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the technologyFast, accurate systems are vital for handling the huge datasets and complex analytical algorithms that are common in modern data science.Python programmers need to boost performance by writing faster pure-Python programs, optimizing the use of libraries, and utilizing modern multi-processor hardware; Fast Python for Data Science shows you how.
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Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data.In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life.Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. Key Features:? Showcases the excellent data science environment in Python. ? Provides examples for readers to replicate, adapt, extend, and improve. ? Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts. Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.
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Classic Computer Science Problems in Python
Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means. Classic Computer Science Problems in Python deepens your Python language skills by challenging you with time-tested scenarios, exercises, and algorithms.As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems Key Features · Breadth-first and depth-first search algorithms · Constraints satisfaction problems · Common techniques for graphs · Adversarial Search · Neural networks and genetic algorithms · Written for data engineers and scientists with experience using Python. For readers comfortable with the basics of Python About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications.Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles.Master these core skills, and you’ll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you’ll face as you grow your skill as a programmer. David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning’s Classic Computer Science Problemsin Swift.
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How can Python be used in computer science education?
Python can be used in computer science education as a beginner-friendly programming language that is easy to learn and understand. It can be used to teach fundamental programming concepts such as variables, data types, loops, and functions. Python's versatility allows students to work on a wide range of projects, from simple text-based programs to more complex applications like web development and data analysis. Additionally, Python's extensive libraries and resources make it a valuable tool for teaching various computer science topics such as artificial intelligence, machine learning, and cybersecurity.
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What is Python in computer science?
Python is a high-level programming language that is widely used in computer science for various applications such as web development, data analysis, artificial intelligence, and scientific computing. It is known for its simplicity and readability, making it a popular choice for beginners and experienced programmers alike. Python's extensive libraries and frameworks make it a versatile language for building a wide range of software applications. Its flexibility and ease of use have contributed to its widespread adoption in the field of computer science.
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What are Python expressions in computer science?
In computer science, Python expressions are combinations of values, variables, and operators that are evaluated to produce a result. These expressions can be simple, such as a single variable or value, or complex, involving multiple operations and functions. Python expressions are used to perform calculations, manipulate data, and make decisions within a program. They are a fundamental concept in programming and are essential for writing effective and efficient code.
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Can someone help me with computer science Python?
Yes, there are many resources available to help you with computer science Python. You can seek help from online tutorials, forums, and communities such as Stack Overflow or Reddit. Additionally, you may consider hiring a tutor or taking an online course to improve your Python skills. Remember that practice and persistence are key to mastering any programming language.
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Foundations of Data Science with Python
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction.This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming.It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets.Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed.The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality. This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics.However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science. Key Features: Applies a modern, computational approach to working with dataUses real data sets to conduct statistical tests that address a diverse set of contemporary issuesTeaches the fundamentals of some of the most important tools in the Python data-science stackProvides a basic, but rigorous, introduction to Probability and its application to StatisticsOffers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
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Data Science and Analytics with Python
Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning.This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook.In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI). This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy.Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play.This edition also addresses crucial issues of explainability, transparency, and fairness in AI.These topics have rightly gained significant attention in recent years.As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount.This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts. Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning.Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook.Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.
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Data Science and Analytics with Python
Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning.This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook.In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI). This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy.Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play.This edition also addresses crucial issues of explainability, transparency, and fairness in AI.These topics have rightly gained significant attention in recent years.As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount.This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts. Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning.Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook.Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.
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Python for Data Science For Dummies
Let Python do the heavy lifting for you as you analyze large datasets Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages.This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond.Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights.This new edition is updated for the latest version of Python and includes current, relevant data examples.Get a firm background in the basics of Python coding for data analysisLearn about data science careers you can pursue with Python coding skillsIntegrate data analysis with multimedia and graphics Manage and organize data with cloud-based relational databasesPython careers are on the rise.Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.
Price: 26.99 £ | Shipping*: 3.99 £
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Can someone help me with Computer Science Python?
Yes, there are many resources available to help you with Computer Science Python. You can seek help from online tutorials, forums, and communities such as Stack Overflow or Reddit. Additionally, you can consider taking online courses or hiring a tutor to guide you through your learning process. Remember that practice and perseverance are key to mastering any programming language.
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What is the topic of computer science Python programming?
The topic of computer science Python programming is the use of the Python programming language to develop software and applications. This includes learning the syntax and structure of Python, understanding how to write and execute code, and applying Python to solve various computational problems. Additionally, the topic covers the use of Python for data analysis, web development, artificial intelligence, and other areas of computer science. Overall, the focus is on using Python as a tool for programming and problem-solving within the field of computer science.
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Can you please provide solutions for computer science Python?
Yes, there are many resources available for learning computer science and Python. You can start by taking online courses on platforms like Coursera, Udemy, or Codecademy. Additionally, there are many books and tutorials available for Python programming, such as "Automate the Boring Stuff with Python" by Al Sweigart or "Python Crash Course" by Eric Matthes. You can also join online communities and forums like Stack Overflow or Reddit to ask questions and get help from experienced programmers. Finally, practicing coding through coding challenges on websites like LeetCode or HackerRank can also help improve your Python skills.
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What is the task in computer science with Python?
The task in computer science with Python can vary widely, as Python is a versatile programming language used for a wide range of applications. Some common tasks in computer science with Python include web development, data analysis, machine learning, artificial intelligence, and automation. Python's simplicity and readability make it a popular choice for beginners and experienced programmers alike, and its extensive libraries and frameworks make it suitable for a diverse set of tasks in computer science.
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